SoroushVahidi/scidocs huggingface dataset:
Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment
(2005)

This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phasereversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.
Brainport: an alternative input to the brain.
(2005)

Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain - and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.
A general framework for brain-computer interface design
(2003)

The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested.
The thought translation device (TTD) for completely paralyzed patients.
(2000)

The thought translation device trains locked-in patients to self-regulate slow cortical potentials (SCP's) of their electroencephalogram (EEG). After operant learning of SCP self-control, patients select letters, words or pictograms in a computerized language support program. Results of five respirated, locked-in-patients are described, demonstrating the usefulness of the thought translation device as an alternative communication channel in motivated totally paralyzed patients with amyotrophic lateral sclerosis.
The Struggle for District-based Health Information Systems in South Africa
(2002)

This article describes the initial period (1994–2001) of an ongoing action research project to develop health information systems to support district management in South Africa. The reconstruction of the health sector in postapartheid South Africa striving for equity in health service delivery and building of a decentralized structure based on health districts. In terms of information systems (IS) development, this reform process translates into standardization of health data in ways that inscribe the goals of the new South Africa by enhancing local control and integration of information handling. We describe our approach to action research and use concepts from actor-network and structuration theories in analyzing the case material. In the detailed description and analysis of the process of IS development provided, we focus on the need to balance standardization and local  exibility (localization); standardization is thus seen as bottom-up alignment of an array of heterogeneous actors. Building on a social system model of information systems, we conceptualize the IS design strategy developed and used as the cultivation of processes whereby these actors are translating and aligning their interests. We develop a modular hierarchy of global and local datasets as a framework within which the tensions between standardization and localization may be understood and addressed. Finally, we discuss the possible relevance of the results of the research in other countries.
Dynamic Conditional Random Fields for Joint Sentence Boundary and Punctuation Prediction
(2012)

The use of dynamic conditional random fields (DCRF) has been shown to outperform linear-chain conditional random fields (LCRF) for punctuation prediction on conversational speech texts [1]. In this paper, we combine lexical, prosodic, and modified n-gram score features into the DCRF framework for a joint sentence boundary and punctuation prediction task on TDT3 English broadcast news. We show that the joint prediction method outperforms the conventional two-stage method using LCRF or maximum entropy model (MaxEnt). We show the importance of various features using DCRF, LCRF, MaxEnt, and hidden-event n-gram model (HEN) respectively. In addition, we address the practical issue of feature explosion by introducing lexical pruning, which reduces model size and improves the F1-measure. We adopt incremental local training to overcome memory size limitation without incurring significant performance penalty. Our results show that adding prosodic and n-gram score features gives about 20% relative error reduction in all cases. Overall, DCRF gives the best accuracy, followed by LCRF, MaxEnt, and HEN.
Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks
(2016)

Vulnerability to emotional disorders including depression derives from interactions between genes and environment, especially during sensitive developmental periods. Adverse early-life experiences provoke the release and modify the expression of several stress mediators and neurotransmitters within specific brain regions. The interaction of these mediators with developing neurons and neuronal networks may lead to long-lasting structural and functional alterations associated with cognitive and emotional consequences. Although a vast body of work has linked quantitative and qualitative aspects of stress to adolescent and adult outcomes, a number of questions are unclear. What distinguishes ‘normal’ from pathologic or toxic stress? How are the effects of stress transformed into structural and functional changes in individual neurons and neuronal networks? Which ones are affected? We review these questions in the context of established and emerging studies. We introduce a novel concept regarding the origin of toxic early-life stress, stating that it may derive from specific patterns of environmental signals, especially those derived from the mother or caretaker. Fragmented and unpredictable patterns of maternal care behaviors induce a profound chronic stress. The aberrant patterns and rhythms of early-life sensory input might also directly and adversely influence the maturation of cognitive and emotional brain circuits, in analogy to visual and auditory brain systems. Thus, unpredictable, stress-provoking early-life experiences may influence adolescent cognitive and emotional outcomes by disrupting the maturation of the underlying brain networks. Comprehensive approaches and multiple levels of analysis are required to probe the protean consequences of early-life adversity on the developing brain. These involve integrated human and animal-model studies, and approaches ranging from in vivo imaging to novel neuroanatomical, molecular, epigenomic, and computational methodologies. Because early-life adversity is a powerful determinant of subsequent vulnerabilities to emotional and cognitive pathologies, understanding the underlying processes will have profound implications for the world’s current and future children.
Mutations in EZH2 cause Weaver syndrome.
(2012)

We used trio-based whole-exome sequencing to analyze two families affected by Weaver syndrome, including one of the original families reported in 1974. Filtering of rare variants in the affected probands against the parental variants identified two different de novo mutations in the enhancer of zeste homolog 2 (EZH2). Sanger sequencing of EZH2 in a third classically-affected proband identified a third de novo mutation in this gene. These data show that mutations in EZH2 cause Weaver syndrome.
CapStones and ZebraWidgets: sensing stacks of building blocks, dials and sliders on capacitive touch screens
(2012)

Recent research proposes augmenting capacitive touch pads with tangible objects, enabling a new generation of mobile applications enhanced with tangible objects, such as game pieces and tangible controllers. In this paper, we extend the concept to capacitive tangibles consisting of multiple parts, such as stackable gaming pieces and tangible widgets with moving parts. We achieve this using a system of wires and connectors inside each block that causes the capacitance of the bottom-most block to reflect the entire assembly. We demonstrate three types of tangibles, called CapStones, Zebra Dials and Zebra Sliders that work with current consumer hardware and investigate what designs may become possible as touchscreen hardware evolves.
Sufficient Sample Sizes for Multilevel Modeling
(2005)

An important problem in multilevel modeling is what constitutes a sufficient sample size for accurate estimation. In multilevel analysis, the major restriction is often the higher-level sample size. In this paper, a simulation study is used to determine the influence of different sample sizes at the group level on the accuracy of the estimates (regression coefficients and variances) and their standard errors. In addition, the influence of other factors, such as the lowest-level sample size and different variance distributions between the levels (different intraclass correlations), is examined. The results show that only a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second-level standard errors. In all of the other simulated conditions the estimates of the regression coefficients, the variance components, and the standard errors are unbiased and accurate.
How Many Trees in a Random Forest?
(2012)

Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets’ density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.
Security in IPv 6-enabled Wireless Sensor Networks : An Implementation of TLS / DTLS for the Contiki Operating System
(2012)

During the last several years the advancements in technology made it possible for small sensor nodes to communicate wirelessly with the rest of the Internet. With this achievement the question of securing such IP-enabled Wireless Sensor Networks (IP-WSNs) emerged and has been an important research topic since. In this thesis we discuss our implementation of TLS and DTLS protocols using a pre-shared key cipher suite (TLS PSK WITH AES 128 CCM 8) for the Contiki operating system. Apart from simply adding a new protocol to the set of protocols supported by the Contiki OS, this project allows us to evaluate how suitable the transport-layer security and pre-shared key management schemes are for IP-WSNs.
Arbuscular mycorrhizal fungi in alleviation of salt stress: a review.
(2009)

BACKGROUND
Salt stress has become a major threat to plant growth and productivity. Arbuscular mycorrhizal fungi colonize plant root systems and modulate plant growth in various ways.


SCOPE
This review addresses the significance of arbuscular mycorrhiza in alleviation of salt stress and their beneficial effects on plant growth and productivity. It also focuses on recent progress in unravelling biochemical, physiological and molecular mechanisms in mycorrhizal plants to alleviate salt stress.


CONCLUSIONS
The role of arbuscular mycorrhizal fungi in alleviating salt stress is well documented. This paper reviews the mechanisms arbuscular mycorrhizal fungi employ to enhance the salt tolerance of host plants such as enhanced nutrient acquisition (P, N, Mg and Ca), maintenance of the K(+) : Na(+) ratio, biochemical changes (accumulation of proline, betaines, polyamines, carbohydrates and antioxidants), physiological changes (photosynthetic efficiency, relative permeability, water status, abscissic acid accumulation, nodulation and nitrogen fixation), molecular changes (the expression of genes: PIP, Na(+)/H(+) antiporters, Lsnced, Lslea and LsP5CS) and ultra-structural changes. Theis review identifies certain lesser explored areas such as molecular and ultra-structural changes where further research is needed for better understanding of symbiosis with reference to salt stress for optimum usage of this technology in the field on a large scale. This review paper gives useful benchmark information for the development and prioritization of future research programmes.
Efficient Object Localization and Pose Estimation with 3D Wireframe Models
(2013)

We propose a new and efficient method for 3D object localization and fine-grained 3D pose estimation from a single 2D image. Our approach follows the classical paradigm of matching a 3D model to the 2D observations. Our first contribution is a 3D object model composed of a set of 3D edge primitives learned from 2D object blueprints, which can be viewed as a 3D generalization of HOG features. This model is used to define a matching cost obtained by applying a rigid-body transformation to the 3D object model, projecting it onto the image plane, and matching the projected model to HOG features extracted from the input image. Our second contribution is a very efficient branch-and-bound algorithm for finding the 3D pose that maximizes the matching score. For this, 3D integral images of quantized HOGs are employed to evaluate in constant time the maximum attainable matching scores of individual model primitives. We applied our method to three different datasets of cars and achieved promising results with testing times as low as less than half a second.
Didier Dubey Suarez Medina Assessment of Web-based Information Security Awareness Courses
(2016)

Information security awareness web-based courses are commonly recommended in cyber security strategies to help build a security culture capable of addressing information systems breaches caused by user mistakes whose negligence or ignorance of policies may endanger information systems assets. A research gap exists on the impact of Information Security Awareness Web-Based Courses: these are failing in changing to a significant degree the behavior of participants regarding compliance and diligence, which translates into continuous vulnerabilities. The aim of this work is to contribute with a theoretical and empirical analysis on the potential strengths and weaknesses of Information Security Awareness Web-Based Courses and with two practical tools readily applicable for designers and reviewers of web-based or mediatized courses on information security awareness and education. The research design seeks to respond two research questions. The first on the formulation of a minimum set of criteria that could be applied to Information Security Awareness Web-Based Courses, to support their real impact on employee’s diligence and compliance, resulting in eleven criteria for courses’ assessment and a checklist. The second, about a controlled experiment to explore the actual impact of an existing course, in respect to diligence and compliance using phishing emails as educational tools, that reaffirms the theoretical assumptions arrived to earlier. The development of minimum criteria and their systematic implementation pursue behavioral change, emphasizes the importance of disciplinary integration in cyber security research, and advocates for the development of a solid security culture of diligence and compliance, capable of supporting the protection of organizations from information system threats. The results gathered in this study suggest that achieving positive results in the existing information security tests that follow security awareness courses does not necessarily imply that diligence or information security policies compliance are affected. These preliminary findings accumulate evidence on the importance of implementing the recommendations formulated in this work.
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
(2016)

Our model achieves the state-of-the-art performance on SUNRGBD, NYUdepth v2. It can be leveraged to improve the groundtruth annotations of newly captured 3943 RGB-D images in the SUNRGBD dataset. 2.2% improvement over previous state-of-the-art average accuracy and best on 15 categories Photometric data is vital for scene labeling while depth is an auxiliary information source. LSMT-CF model is important for fusing context features from different sources.
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks
(2018)

Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D lightweight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed lightweight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+DNTU (NTU) dataset, we achieve 93.2% and 97.6% for crosssubject and cross-view measurement, and on the NorthwesternUCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view.
Learning from Ambiguously Labeled Face Images
(2018)

Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled faces. We further extend MCar to incorporate the labeling constraints among instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvements on several ambiguously labeled datasets.
Using Generative Adversarial Networks to Design Shoes : The Preliminary Steps
(2017)

In this paper, we envision a Conditional Generative Adversarial Network (CGAN) designed to generate shoes according to an input vector encoding desired features and functional type. Though we do not build the CGAN, we lay the foundation for its completion by exploring 3 areas. Our dataset is the UT-Zap50K dataset, which has 50,025 images of shoes categorized by functional type and with relative attribute comparisons. First, we experiment with several models to build a stable Generative Adversarial Network (GAN) trained on just athletic shoes. Then, we build a classifier based on GoogLeNet that is able to accurately categorize shoe images into their respective functional types. Finally, we explore the possibility of creating a binary classifier for each attribute in our dataset, though we are ultimately limited by the quality of the attribute comparisons provided. The progress made by this study will provide a robust base to create a conditional GAN that generates customized shoe designs.